The future of paramedic education: Problematizing the <i>translucent</i> curriculum in paramedicine
Bibliographic record
Abstract
This article questions the extent to which paramedic education is adequate for a changing prehospital and ambulance world and to more advanced forms of professionalism. Paramedic training and education has increasingly moved out of in-service provision. In most Anglophone societies that feature similar models of prehospital medicine, the route to the qualification of new paramedics is through university degree programmes or college certification. This is an important route for professionalizing the paramedic occupation and has served to broaden the scope of practice and to boost the status of the paramedic. There remains much to do, however, in terms of modernizing and strengthening the provision of paramedic education. Drawing on the classic sociological notion of the hidden curriculum, this article argues that reform of paramedic education is an essential element in better preparing the paramedic profession for the future. Paramedic education needs to pivot away from its overwhelming emphasis on biomedical positivism and what we call the tyranny of the bio-psycho-medico in order to develop a more sociologically-informed curriculum that better prepares students for the realities of what they meet on the streets – a reality that better aligns with community paramedicine – in a changing society, and to provide scope for a more Socratic introspection of the nature, culture, structure, and ethics of the paramedic role itself.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".